Learning to see, seeing to learn: Visual aspects of sensemaking

Abstract

When one says "I see," what is usually meant is "I understand." But what does it mean to create a sense of understanding a large, complex, problem, one with many interlocking pieces, sometimes ill-fitting data and the occasional bit of contradictory information? The traditional computer science perspective on helping people towards understanding is to provide an armamentarium of tools and techniques - databases, query tools and a variety of graphing methods. As a field, we have an overly simple perspective on what it means to grapple with real information. In practice, people who try to make sense of some thing (say, the life sciences, the Middle East, the large scale structure of the universe, their taxes) are faced with a complex collection of information, some in easy-to-digest structured forms, but with many relevant parts scattered hither and yon, in forms and shapes too difficult to manage. To create an understanding, we find that people create representations of complex information. Yet using representations relies on fairly sophisticated perceptual practices. These practices are in no way preordained, but subject to the kinds of perceptual and cognitive phenomena we see in every day life. In order to understand our information environments, we need to learn to perceive these perceptual elements, and understand when they do, and do not, work to our advantage. A more powerful approach to the problem of supporting realistic sensemaking practice is to design information environments that accommodate both the world's information realities and people's cognitive characteristics. This paper argues that visual aspects of representation use often dominate sensemaking behavior, and illustrates this by showing three sensemaking tools we have built that take advantage of this property.